Towards Taming Big Data Variety: From Social Networks to Brain Networks

Abstract

Over the past decade, we are experiencing big data challenges in various research domains. The data nowadays involve an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Taming datavariety issues is essential to many research fields, such as biomedical research, social computing, neuroscience, business intelligence, etc. The data varietyissues are difficult to solve because the data usually have complex structures, involve many different types of information, and multiple data sources. In this talk, I'll briefly introduce the big data landscape and present two projects that help us better understand how to solve data variety issues in different domains. The first project addresses the challenge of integrating multiple data sources in the context of social network research. Specially, I will describe a network alignment method which exploit heterogeneous information to align the user accounts across different social networks. The second project addresses the challenge of analyzing complex data types in the context of brain network research. I will model the functional brain networks as uncertain graphs, and describe a subgraph mining approach to extract important linkage patterns from the uncertain graphs. I'll also introduce future work in this direction and explain some possibilities for upcoming evolutions in big data research.

Bio

Xiangnan Kong is an assistant professor at the Worcester Polytechnic Institute in the Computer Science Department. He received his PhD degree (2014) from University of Illinois at Chicago in computer science. His research interests are in data mining and big data analysis, with emphasis on addressing the data variety issues in biomedical research and social computing. In 2009, he joined the Big Data and Social Computing Lab at University of Illinois, Chicago, where he has been working on mining graph data in the domains of neuroscience, biomedical informatics and social networks. Since then, he has published more than 60 papers in data mining conferences and journals, including KDD, ICDM, SDM, WWW, WSDM, CIKM, TKDE. Two of his papers on graph mining was selected as best ICDM papers for publication in KAIS Journal.